The invention relates to a system and methods for managing and viewing historical security analyst data; for measuring, analyzing, and tracking the historical performance of security analysts"" estimates and buy/sell recommendations; and using such performance and other information to automatically produce better predictors of future corporate earnings or stock-price performance.
There are many individuals who analyze financial data and financial instruments, such as equity and fixed-income securities. At least some of these individuals analyze such data in an attempt to predict future economic events. Such individuals may include, for example, security analysts and may be known as contributors or analysts, among others. The role of the security analyst is generally well-known and includes, among other things, the issuance of earnings or other financial estimates concerning future economic events and recommendations on whether investors should buy, sell, or hold financial instruments, such as equity securities. Security analyst estimates may include, but are not limited to, quarterly and annual earnings estimates for companies, whether or not they are traded on a public securities exchange.
At least some investors tend to rely on the earnings estimates and recommendations issued by security analysts. Usually more than one analyst follows a given equity security. Analysts often disagree on their earnings estimates and recommendations and, as a result, analysts"" earnings estimates and recommendations may sometimes vary.
A number of financial information services providers (FISPs) gather and report analysts"" earnings estimates and recommendations. At least some FISPs report the high, low, and mean earnings estimates, as well as mean recommendations for equity securities (as translated to a FISP""s particular scale, for example, one to five). In addition, FISPs may also provide information on what the earnings estimates and recommendations were seven and thirty days prior to the most current consensus, as well as the differences between the consensus for a single equity security and that of the relevant industry. Moreover, for some clients, FISPs provide earnings estimates and recommendations on an analysts-by-analyst basis. An advantage of the availability of analyst-level estimates and recommendations is that a client can view the components of the mean estimate or recommendation by analyst. Various drawbacks exist, however, with these approaches and other known techniques.
For example, prior approaches include a software program that displays all current estimates. For a particular fiscal period for a particular security the software provides the ability to simply xe2x80x9cincludexe2x80x9d or xe2x80x9cexcludexe2x80x9d each estimate (recommendation) from the mean. This is problematic for several reasons. First, commercially available databases of estimates and recommendations contain xe2x80x9ccurrentxe2x80x9d data on thousands of stocks. Each stock may have estimates from 1 to 70 or more analysts. Each analyst may provide estimates for 1 to many periods. The data may be updated throughout the day. Manually dealing with this volume of information can be time consuming.
A second drawback is that with current techniques, if someone were inclined to determine which estimates (recommendations) should get more weight, and which estimates should get less or no weight, the sheer volume of analysts (over 3,000 for U.S. stocks alone) makes it extremely difficult to know which analysts provide more useful information than others. Current techniques lack sufficient ability to intelligently measure historical analyst performance and beneficially use such measurements.
A third drawback is that it while it is possible to imagine various weighting systems or algorithms, it is difficult to effectively implement or test them. Current systems do not provide the ability to effectively devise new estimate (recommendation) weighting algorithms; nor do they provide the ability to easily test their (hypothetical) historical performance.
A fourth drawback with current techniques is that there are limited or no tools for effectively viewing historical estimates and recommendations as time-series graphs or for overlaying this information over a graph of prices for the securities to understand the relationship between changes in estimates (recommendations) to changes in securities prices. These and other drawbacks exist with existing systems
An object of the invention is to overcome these and other drawbacks of prior approaches and techniques.
Another object of the invention is to measure and use the historical performance of an analyst""s past estimates or recommendations to better predict future earnings or effectively use analysts"" recommendations.
Another object of the invention is to provide a tool to automatically create more accurate composite estimates (predictors) by adjusting one or more analyst""s estimates up (down) if they have a historical tendency to under (over) estimate the value of future quantities such as earnings.
Another object of the invention is to automatically create an improved composite estimate for one or more securities by calculating a weighting factor for each analyst""s estimate that gives relatively more weight to certain analyst estimates and relatively less or no weight to other analyst estimates, where the weighting factor is based upon predetermined criteria.
Another object of the invention is to objectively measure the historical accuracy of the estimates made by one or more Contributors (analysts and/or brokers)
Another object of the invention is to provide a tool to measure historical accuracy of predictions with the flexibility for the user to specify one or more of the time frame of estimates to be measured (e.g., those estimates 9-12 months prior to actual report date), the number of periods over which to aggregate performance, the error metric used to calculate performance, and the stocks over which to aggregate performance.
Another object of the invention is to exclude from calculation of an automatically generated composite all estimates (recommendations) that do or do not meet certain criteria.
Another object of the invention is to provide a tool to automatically identify a cluster, or major revision of estimates (or recommendations), based upon predetermined criteria
Another object of the invention is to automatically create a composite estimate (or recommendation) by excluding (or assigning reduced weight) to those estimates received prior to the beginning of a cluster.
Another object of the invention is to automatically calculate an improved composite estimate or recommendation by both adjusting estimates of earnings by calculating an earnings estimate based upon the adjustment and weighting factors for a plurality of analysts"" estimates.
Another object of the invention is to measure the historical performance of a single or plurality of analysts"" estimates and to measure the historical profitability of recommendations of either a single or plurality of analysts.
Another object of the invention is to compare the performance of analysts"" estimates and recommendations for a particular financial instrument or industry.
Another object of the invention is to enable a user to define the method for measuring the performance of an analyst by allowing for the specification of a number of security analyst performance parameters or metrics.
Another object of the invention is to allow for the creation, storage, and recall of security autoweight models and related estimates where the models automatically assign weights to each analyst""s estimate or recommendation based on stored, user-defined criteria.
Another object of the invention is to test autoweight models by applying predetermined criteria.
Another object of the invention is to provide a data visualization technique to allow a user to display simultaneously the estimates of earnings (or other quantities such as revenues) for a single or plurality of contributors using predetermined criteria, along with the actual earnings (or other quantity) corresponding to those estimates and other related parameters or metrics.
Another object of the invention is to provide a data visualization technique to allow a user to display simultaneously time-series charts of estimates of earnings (or of estimates of other quantities such as revenues, or of recommendations) for a single or plurality of contributors using predetermined criteria, along with a time-series of the security""s price over the corresponding time interval.
Another object of the invention is to provide a data visualization technique to allow one to display, as either raw numerical data, a chart, or graph, a number of earnings estimate performance metrics for either a single or plurality of analysts, based upon predetermined criteria.
Another object of the invention is to provide a data visualization technique to display simultaneously the numerical representation of a single or plurality of security analysts"" purchase recommendations for predetermined criteria, along with the actual change in the value of the security corresponding to the recommendation(s).
These and other objects of the invention are carried out, according to various preferred embodiments of the invention.
According to one embodiment of the invention, an improved estimate of a future earnings event can be automatically developed for one or a plurality of securities by applying a weighting factor to each analyst""s estimate. The weighting factor may be based on a variety of factors as specified in an autoweight model such as the relative recency of each analyst""s estimate, the analyst""s historical performance, or other factors. For example, if an estimate is relatively old, it may get a relatively low or zero weighting, whereas more recent estimates may be given a relatively high weighting. The sum of the weights assigned to analyst""s estimates for a particular fiscal period should equal one. Two or more factors may be used in combination. Autoweight models allow for the definition of an arbitrary number of different factors. For example, an analyst may receive a score for her past performance and another score for the recency of her estimate. Using a pre-defined function, these factor scores can be consolidated with the result being a summary weight for each analyst. Using the automatically calculated weights, the estimates and revisions of either a single or plurality of analysts"" can be composited for a given fiscal period by calculating the weighted average of estimates such that an improved estimate can be calculated. For example, a custom composite estimate or composite estimate may be calculated by multiplying a plurality of analysts"" current earnings estimates for a particular security by their respective weighting factors and then summing over each estimate. Similarly, analyst recommendations at a point in time can be automatically weighted according to different factors to create an improved composite recommendation by multiplying each analyst""s recommendation by that analyst""s weighting factor.
According to another aspect of the invention, a system and method enable a user to track, analyze, and compare analysts"" past performances. According to this embodiment, a database may be provided that contains information about analyst""s past performance through the combination of each analyst estimate record in the database has a number of predetermined data fields.
The database may comprise a combination of estimate data comprising raw data regarding estimates and performance for analysts and a pre-calculated values that are calculated and stored in the database for further analysis. The pre-calculated values may be based on the estimate data loaded from a secondary source analyst performance database or maintained on that database from a transaction processing system, for example.
These pre-calculated values may include error metric values for each security, for each historical fiscal period in the database, for each contributor (e.g., analyst/broker pair) is a row in the database. Each row comprises multiple error metrics valued over a range of time periods. For example, if three error metrics were provided, the system may maintain a value for error metric 1 over a 0-3 month time period, error metric 1 over a 3-6 month time period, error metric 2 over a 0-3 month time period, error metric 2 over a 3-6 month time period, error metric 3 over a 0-3 month time period, and error metric 3 over a 3-6 month time period. Of course, a great number of such error metrics may be stored and the number and ranges of the time periods may also vary according to the present invention.
Example fields for the pre-calculated portion of the database may comprise an analyst identifier, an event identifier, an analyst estimate date, a raw error indicator (analyst estimate minus the actual earnings for a particular event), other error metrics, such as the error percent to actual earnings, error percent to consensus, other user-defined error metrics, and the number of days between the estimate of an event and the actual event. This last parameter can provide a significant advantage with respect to aspects of the present invention because, in many cases, a more recent earnings estimate or revision is likely to be more accurate than an estimate made months prior to an earnings event. Estimates made prior to an earnings event may be classified according to Earliness Time Bins, where each Time Bin represents a range of days preceding an earnings event. For example, one Earliness Time Bin may include all estimates made between 0 and 90 days prior to an earnings event.
By using this analysts"" past performance database, the invention enables a user to rank, measure, and analyze analysts"" historical performances based upon any metric, including a comparison of all or a subset of analysts within an Earliness Time Bin; a comparison of selected analysts across several Earliness Time Bins; scatterplots of percent errors versus number of days early for a single or plurality of analysts; and other comparisons.
According to one embodiment, the invention allows for the rapid visualization and analysis of analysts"" estimates or recommendations by creating and maintaining indices for predetermined data relationships, pre-calculating and storing predetermined analyst performance metrics, and calculating, compressing and storing time series estimates and summary measures of those estimates.
According to another aspect of the invention, a front-end graphical user interface (GUI) is provided to facilitate analysis of analysts"" prior performance for one or more securities. Preferably, the prior estimates are stored in a database that includes, but is not limited to, fields corresponding to a security identifier for each security; a plurality of earnings events, such as, for example, the issuance of a company""s actual quarterly or yearly earnings reports; earnings event dates; an analyst and broker identifier; and predetermined periods of times preceding an earnings event. Other fields and types of data also may be included. The front-end GUI will allow a user to select easily a security, earnings event, event date, Earliness Time Bin, and Contributors for analysis. The retrieved analysis information may be viewed as either raw data or, by using a data visualization technique, as a chart or graph.
According to one aspect of this embodiment of the invention, each analysts"" estimates and revisions thereto are displayed simultaneously, along with the actual earnings of the companies they follow. Preferably, each analysts"" estimate is plotted on a graph displaying the estimate (in dollars and cents) on the vertical or y axis and time (in days) on the horizontal or x axis. More specifically, each analyst""s estimate is displayed as a horizontal line at a level corresponding to the estimate and having a length equal to the number of days that the analyst""s estimate was at that level. If any analyst revises that estimate, a new horizontal line is displayed at the new level and the two lines are connected by a vertical line, such that the plot takes the form of a step function.
Other levels of control may be provided including displays of derived time series such as high estimates, mean estimates, low estimates, and/or Composite estimates with or without a simultaneous display of actual earnings. To further facilitate the viewing of such data, each estimate, whether reflecting the determination of an individual analyst or a derived estimate, may be assigned a unique color. A legend box may also be displayed simultaneously with a chart or graph that indicates which color is assigned to which estimate. Selecting an analyst or derived estimate series in the legend highlights (emboldens) the corresponding time series in the chart. The user may show or hide individual estimate series by means of on/off controls in the legend. The user may sort the legend by analyst name, broker name, or other criteria. Advanced navigation techniques include selecting an analyst from the legend and issuing a command (e.g., from a right-click pop-up menu) to jump to a detailed display of historical performance for the selected analyst. The user has the ability to arbitrarily change the scale of viewing and can zoom in to fill the screen with two days of information or zoom out to see five years of information. Optionally, a chart of the selected securities prices can be displayed on a chart below the estimates chart. The horizontal (time) axes of the two chart are synchronized so that zooming one chart zooms. This technique is valuable for understanding the impact on changes of estimates (or derived estimates) on changes in security price. Viewing historical estimates in this fashion may provide context and thus aid in the understanding of an analyst""s performance track record and estimate revision patterns. This information can be used valuably when deciding how to appraise changes in an analyst""s current estimates. This information can also be used valuably in building understanding of estimate and recommendation changes in general and therefore help the user create more valuable autoweight models.
According to another embodiment of the invention, a user may rank, measure, and analyze the profitability of analysts"" recommendations regarding the advisability of purchasing, selling, or holding a particular security at any given time. More specifically, the system allows a user to control, manipulate, and otherwise refine the normalization and translation of the recommendation descriptions of individual analysts to the scales published by FISPs such as First Call or IBES, which are used generally in the financial and business communities. In addition, weighting factors, similar to the ones described above relating to earnings estimates, and/or adjustment factors may be calculated for analysts"" recommendations. Therefore, the system enables a user to view an analyst""s xe2x80x9ccorrectedxe2x80x9d estimate through the use of the adjustment and weighting factor. The system also enables a user to compare and chart the profitability of following the recommendation of one analyst versus that of another analyst or the average recommendation. In addition, users may create portfolio-creation rules to determine when and how much of a security to buy or sell and, furthermore, to track the value and test the profitability of having carried out such rules for a single or plurality of analysts over any given time period.